Mahesh Venkata Krishna

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Temporal segmentation of videos into meaningful image sequences containing some particular activities is an interesting problem in computer vision. We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup. We propose using one-class(More)
This paper propose a novel framework for unsupervised detection of object interactions in video sequences based on dynamic features. The goal of our system is to process videos in an unsupervised manner using Hierarchical Bayesian Topic Models, specifically the Hierarchical Dirichlet Processes (HDP). We investigate how low-level features such as optical(More)
Recent approaches in traffic and crowd scene analysis make extensive use of non-parametric hierarchical Bayesian models for intelligent clustering of features into activities. Although this has yielded impressive results, it requires the use of time consuming Bayesian inference during both training and classification. Therefore, we seek to limit Bayesian(More)
We propose a hierarchical Bayesian model-the wordless Hierarchical Dirichlet Processes-Hidden Markov Model (wHDP-HMM), to tackle the problem of unsupervised cell phenotype clustering during the mitosis stages. Our model combines the unsupervised clustering capabilities of the HDP model with the temporal modeling aspect of the HMM. Furthermore, to model cell(More)
—In various real-world applications of distributed and multi-view vision systems, ability to learn unseen actions in an online fashion is paramount, as most of the actions are not known or sufficient training data is not available at design time. We propose a novel approach which combines the unsu-pervised learning capabilities of Hierarchical Dirichlet(More)
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